170 citations · 506 across the 51 of their papers we have counts for
8 papers · 1 filter
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming
Alec Koppel, Amrit Singh Bedi, Bhargav Ganguly +1
In tabular multi-agent reinforcement learning with average-cost criterion, a team of agents sequentially interacts with the environment and observes local incentives. We focus on t…
Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication
James Di, Ehsan Zobeidi, Alec Koppel +1
Multi-agent mapping is a fundamentally important capability for autonomous robot task coordination and execution in complex environments. While successful algorithms have been prop…
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach
Qinbo Bai, Amrit Singh Bedi, Mridul Agarwal +2
Reinforcement learning is widely used in applications where one needs to perform sequential decisions while interacting with the environment. The problem becomes more challenging w…
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference
Michael E. Kepler, Alec Koppel, Amrit Singh Bedi +1
Gaussian processes (GPs) are a well-known nonparametric Bayesian inference technique, but they suffer from scalability problems for large sample sizes, and their performance can de…
MARL with General Utilities via Decentralized Shadow Reward Actor-Critic
Junyu Zhang, Amrit Singh Bedi, Mengdi Wang +1
We posit a new mechanism for cooperation in multi-agent reinforcement learning (MARL) based upon any nonlinear function of the team's long-term state-action occupancy measure, i.e.…
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang +2
Reinforcement learning is a framework for interactive decision-making with incentives sequentially revealed across time without a system dynamics model. Due to its scaling to conti…